EPAM Data Strategy & Governance

Organizations building modern data platforms often struggle with fragmented data landscapes, inconsistent definitions of business data, limited visibility into data lineage, and unclear ownership of critical data assets. EPAM’s Data Strategy & Governance implementation helps enterprises establish a scalable Data Governance framework powered by Microsoft Purview, enabling organizations to discover, classify, govern, and trust their data across the enterprise. This engagement is designed for Chief Data Officers, data platform leaders, enterprise architects, governance teams, and analytics organizations that need a unified approach to managing data assets across analytics, AI, and operational workloads.

By establishing a governed data ecosystem, organizations gain improved data discoverability, faster analytics development, stronger regulatory compliance, and higher trust in enterprise data assets. The implementation also creates a strong foundation for advanced analytics, AI adoption, and modern data platforms such as Microsoft Fabric and Azure data services.

What You Will Receive

• Data Governance Maturity Assessment: Evaluation of the current data governance landscape, identification of governance risks and gaps, and definition of a target-state data governance roadmap aligned with regulatory, operational, and business goals.
• Data Governance Operating Model: Definition of governance roles, responsibilities, stewardship workflows, RBAC access models, escalation processes, and policy frameworks aligned with enterprise governance standards.
• Microsoft Purview Platform Implementation: Deployment and configuration of Microsoft Purview including data catalog setup, automated scanning, metadata enrichment, classification policies, and enterprise data discovery.
• Data Lineage and Metadata Management: Implementation of automated Data Lineage capabilities across integrated data sources to provide traceability of data flows, transformations, and dependencies across the enterprise data platform.
• Data Quality and Stewardship Enablement: Creation of business glossaries, stewardship workflows, initial data quality rules, governance dashboards, and operational runbooks to support day-to-day governance operations.

Typical Implementation Approach

Phase 1 — Governance Assessment

• Assess the current data governance maturity, tooling landscape, and organizational processes.
• Identify critical data assets, governance gaps, and regulatory compliance requirements.
• Deliver a governance maturity assessment report and target-state governance architecture.

Phase 2 — Operating Model Design

• Define governance roles including data owners, stewards, and governance committees.
• Design stewardship workflows, governance policies, and RBAC-based access models.
• Develop the enterprise data governance roadmap and KPIs for governance adoption.

Phase 3 — Microsoft Purview Enablement

• Deploy and configure Microsoft Purview within the customer environment.
• Connect priority data sources and enable cataloging, scanning, and metadata ingestion.
• Activate classification, policy management, and automated data discovery.

Phase 4 — Governance Operationalization

• Implement business glossary and metadata governance frameworks.
• Enable Data Lineage visualization across data pipelines and analytics platforms.
• Deliver governance dashboards, operational runbooks, and training for governance teams.

Expected Outcomes

• A scalable governance foundation powered by Microsoft Purview to support future analytics and AI initiatives.
• Improved enterprise data trust, compliance readiness, and standardized governance practices.
• Faster analytics and AI solution delivery due to improved data discovery and metadata visibility.
• Accelerated issue resolution through field-level Data Lineage and improved data observability.
• Increased productivity for data governance teams through automated cataloging and metadata enrichment.